A multi-band EMI electromagnetic interference source identification and positioning method

Abstract

The application provides a multi-band EMI electromagnetic interference source identification and positioning method, comprising: preprocessing and standardizing electromagnetic characteristic data, real-time environmental data of a sensor network, and running state data of an electronic device; based on a predicted electromagnetic environment perception result, extracting electromagnetic environment characteristic data, denoising and preprocessing electromagnetic environment characteristic data of different dimensions, and performing weight distribution and optimized combination, and obtaining interference source position coordinates through iterative calculation; applying the optimized sensor array layout and parameter setting to the test process of the electronic device, obtaining performance parameters of the electronic device under different electromagnetic environments, the performance parameters including electromagnetic compatibility and anti-interference capability, and comparing and calibrating the test results with preset standard values.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a multi-band EMI electromagnetic interference source identification and positioning method. BACKGROUND

[0002] In an electronic device test laboratory, in order to achieve comprehensive perception and analysis of the electromagnetic environment, it is necessary to integrate the electromagnetic characteristic data of the device and the real-time environmental data of the sensor network. However, in actual operation, due to the complex and changeable electromagnetic environment in the laboratory, the electromagnetic characteristics of various devices are quite different, and the layout of the sensor array is also difficult to optimize, resulting in many technical problems in the data collection and analysis process. First, different types and models of devices in the laboratory will produce various electromagnetic interferences during operation, and the frequency characteristics, time characteristics and spatial distribution characteristics of these interference sources are different, so how to accurately locate and characterize the interference sources is a big problem. Secondly, in order to comprehensively perceive the electromagnetic environment, a large number of sensors need to be deployed in the laboratory, but the layout of the sensor array will directly affect the accuracy and efficiency of data collection, so how to optimize the layout of the sensor array to obtain high-quality data is also a difficult problem. Thirdly, in the data analysis process, how to effectively fuse the electromagnetic characteristic data of the device with the environmental data collected by the sensor network, and combine the running state of the device for comprehensive analysis, so as to realize accurate characterization of the electromagnetic environment and precise positioning of the interference source, is also a technical problem to be solved. Finally, due to the complexity and uncertainty of the electromagnetic environment, how to use the results of perception and analysis to calibrate the test results of the device to improve the accuracy and reliability of the test is also a problem worthy of in-depth study. SUMMARY

[0003] The present application provides a multi-band EMI electromagnetic interference source identification and positioning method, mainly including:

[0004] The acquired electromagnetic characteristic data, real-time environmental data of the sensor network and running state data of the electronic device are preprocessed and standardized.

[0005] By collecting electromagnetic field intensity data generated by various running devices in the laboratory, frequency characteristics of different interference sources, time-varying characteristics of electromagnetic signals and spatial distribution relationship information of electronic devices, an interference source probability distribution model for distinguishing and identifying multi-source interference is constructed, and each interference source in the laboratory electromagnetic environment is characterized and spatially positioned.

[0006] The standardized electronic equipment electromagnetic characteristic data, sensor network real-time environment data and electronic equipment running state data are fused, input into a trained and optimized interference source probability distribution model, an electromagnetic environment perception result is predicted and output, and the electromagnetic environment perception result is visualized and analyzed;

[0007] Based on the predicted electromagnetic environment perception result, electromagnetic environment feature data is extracted, different dimension electromagnetic environment feature data is denoised and preprocessed, weight distribution and optimization combination are performed, and interference source position coordinates are obtained through iterative calculation;

[0008] According to the interference source position coordinates, the array of the sensor network is evaluated in different regions, the regions with detection coverage or signal quality lower than the preset requirement are identified, the sensor positions and orientations in the preset range of the identified regions are adjusted, the sensitivity parameters of the sensors are adjusted according to the electromagnetic environment perception result, and in the adjustment process, the detection effect of each region is evaluated and analyzed, and the sensor array layout and parameters are optimized;

[0009] The optimized sensor array layout and parameter setting are applied to the test process of the electronic equipment, the performance parameters of the electronic equipment in different electromagnetic environments are obtained, the performance parameters include electromagnetic compatibility and anti-interference ability, and the test result is compared and calibrated with the preset standard value.

[0010] The technical scheme provided by the embodiment of the application can include the following beneficial effects:

[0011] The application discloses a multi-band EMI electromagnetic interference source identification and positioning method. Based on the model prediction result, the application extracts electromagnetic environment features, calculates interference source positions, and performs regional evaluation and optimization on the sensor network. Through adjusting the sensor positions, orientations and sensitivity parameters, the application improves the detection coverage and signal quality. Finally, the optimized sensor layout is applied to the electronic equipment test to evaluate the electromagnetic compatibility and anti-interference ability. The application realizes accurate electromagnetic environment perception, accurate interference source positioning and dynamic optimization of the sensor network, effectively improves the performance and reliability of the electronic equipment in a complex electromagnetic environment. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 The application discloses a multi-band EMI electromagnetic interference source identification and positioning method. DETAILED DESCRIPTION

[0013] The technical solutions of the present application will be described clearly and completely below in connection with the embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, but not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0014] As Figure 1 The method for identifying and positioning an electromagnetic interference (EMI) source based on multiple frequency bands can specifically include the following steps.

[0015] In S101, the acquired electromagnetic characteristic data, real-time environmental data of the sensor network, and running state data of the electronic device are preprocessed and standardized.

[0016] The electromagnetic characteristic data of the electronic device is acquired from a plurality of electromagnetic sensor nodes, and the electromagnetic characteristic data records time sequence information at a preset sampling frequency. Wavelet denoising processing is performed on the electromagnetic characteristic data to obtain first type of processed data. Environmental temperature and humidity data and illumination intensity data are collected by a distributed sensor network and subjected to mean filtering to obtain second type of processed data. The first type of processed data and the second type of processed data are time-aligned by time stamp. The correlation between the first type of processed data and the second type of processed data is calculated to obtain an abnormal data set. The abnormal data set is segmented by using a fixed-length sliding time window. The segmented data is clustered to obtain a device state feature vector. A running state corresponding relationship database is established according to the device state feature vector.

[0017] Specifically, electromagnetic characteristic data generated by the electronic device at different operating frequency bands is acquired from multiple electromagnetic sensor nodes, and the electromagnetic characteristic data is recorded in time sequence according to a preset sampling frequency, while environmental temperature and humidity data and illumination intensity data are collected through a distributed sensor network, and the collected data is stored according to a unified time stamp. Wavelet denoising processing is performed on the collected electromagnetic characteristic data to obtain first type of processed data, and a comparison between the amplitude of the first type of processed data and a preset threshold value is used to determine the electromagnetic interference level, while mean value filtering is performed on the environmental temperature and humidity data and the illumination intensity data to obtain second type of processed data. Device operating parameters are acquired from an electronic device operating controller and data standardization is performed to obtain third type of processed data, and the first type of processed data, the second type of processed data and the third type of processed data are time sequence aligned through the time stamp. A multiple regression model is established based on the time sequence aligned data, a correlation degree between the electromagnetic characteristic data and the environmental data is determined, and data points with a correlation degree exceeding a preset range are marked as abnormal to obtain an abnormal data set. The abnormal data set is segmented through a fixed length sliding time window, and device state feature vectors are obtained through density clustering of the segmented data, and a feature vector and device operating state correspondence relationship database is established. Real-time collected electromagnetic characteristic data, environmental data and operating parameter data are subjected to state recognition according to the feature vector and device operating state correspondence relationship database, and a device operating state determination result is obtained. The electromagnetic characteristic data collection involves the arrangement of numerous sensor nodes, multiple electromagnetic sensors are arranged around the electronic device at different azimuth angles, the sensor spacing is 20 cm, the sampling frequency is set to 100 Hz, and the electromagnetic field strength is monitored in all directions. The electromagnetic data acquired by each sensor node includes amplitude and phase information, the amplitude range is between 0-100 mV, and the phase range is between 0-360 degrees, and a millisecond level time stamp is added during data recording. At the same time, temperature sensors are arranged in the sensor network to collect environmental temperature data, the measurement range is -40 to 85 degrees Celsius, the accuracy is 0.1 degrees Celsius, the humidity sensor measurement range is 0-100%, the accuracy is 1%, and the illumination sensor measurement range is 0-65000 lux. The wavelet denoising processing of the electromagnetic characteristic data uses db4 wavelet basis function, 4 layers of decomposition are set, and high frequency noise is suppressed. The electromagnetic interference level division uses a segmented threshold judgment method, 0-20 mV is defined as low interference, 20-50 mV is defined as medium interference, and 50 mV and above is defined as high interference. The mean value filtering of the environmental data uses a 5 second sliding window to reduce the influence of instantaneous fluctuations. The device operating parameters include voltage, current, power factor and other key indicators, the voltage measurement range is 0-380V, the current measurement range is 0-100A, and the power factor range is 0-1. The data standardization uses the maximum and minimum value normalization method to map all parameters to the 0-1 interval. The data time sequence alignment uses the nearest principle to match data from different sources according to the time stamp, and the maximum time difference is allowed to be 50 milliseconds.In the multiple regression model analysis, the electromagnetic characteristic data is taken as the dependent variable, and the environmental data is taken as the independent variable to establish a linear relationship model. The correlation degree is measured by Pearson correlation coefficient. When the absolute value of the correlation coefficient exceeds 0.8, it is determined as strong correlation, and when it is lower than 0.3, it is determined as weak correlation. The abnormal data marking is performed on the data points whose correlation coefficient deviates significantly from the historical mean. The sliding time window length is set to 60 seconds, and the window sliding step is 10 seconds. The density clustering adopts the DBSCAN algorithm, and the neighborhood radius is set to 0.1, and the minimum sample number is 5 points. The equipment state feature vector contains 10 dimensions, respectively corresponding to the statistical characteristics of different operating parameters. The state recognition result is divided into normal operation, load fluctuation, efficiency reduction and other categories, and each category of state has a corresponding feature value range definition. In the operation state monitoring of an industrial motor, 500,000 valid data were collected during a 3-month operation period, and 85 abnormal states were successfully identified, of which 78 were consistent with the actual fault records, verifying the reliability of the monitoring method. The average response time of the operation state determination result is 200 milliseconds, which meets the real-time monitoring requirements.

[0018] S102, by collecting electromagnetic field intensity data generated by various operating equipment in the laboratory, frequency characteristics of different interference sources, time-varying characteristics of electromagnetic signals, and spatial distribution relationship information of electronic equipment, a probability distribution model of interference sources is constructed to distinguish and identify multiple source interference, and each interference source in the laboratory electromagnetic environment is characterized and spatially located.

[0019] Obtain the original electromagnetic field data collected by the distributed electromagnetic sensor array, and perform Fourier transform on the original electromagnetic field data to obtain frequency spectrum feature data; extract interference source characteristic frequency components from the frequency spectrum feature data to obtain first type feature data, and perform independent component analysis on the first type feature data to obtain independent signal source data; construct a mixed probability density function with multiple Gaussian components according to the independent signal source data, update the Gaussian component parameters by expectation maximization iterative calculation to obtain an interference source probability distribution model; spatially locate each Gaussian component in the interference source probability distribution model by using a triangular positioning method, and perform spatial interpolation processing on signal intensity data obtained from the electromagnetic sensor array to obtain electromagnetic field spatial distribution data; calculate the electromagnetic field gradient of each sampling point according to the electromagnetic field spatial distribution data, and extract time domain mutation characteristics and periodic characteristics in combination with the independent signal source data to obtain interference source time-varying characteristic data.

[0020] Specifically, the original electromagnetic field data is collected synchronously by the distributed electromagnetic sensor array at multiple spatial positions in the laboratory, and the electromagnetic field intensity and phase information of each sampling point are recorded according to the preset sampling time sequence. The frequency spectrum characteristic data is obtained by Fourier transform of the original electromagnetic field data. The adaptive frequency threshold is set according to the frequency spectrum characteristic data, and the first type of characteristic data is extracted by extracting the characteristic frequency components of the interference source. The independent component analysis is performed on the first type of characteristic data of each sampling point to obtain multiple independent signal source data. The mixed probability density function with multiple Gaussian components is constructed based on the independent signal source data, and the Gaussian component parameters are updated by iterative calculation of the expectation maximization algorithm to obtain the optimized interference source probability distribution model. The triangular positioning method is used to locate the interference source corresponding to each Gaussian component, and the signal intensity data is obtained from multiple electromagnetic sensor array nodes for spatial interpolation to obtain the electromagnetic field spatial distribution data. The electromagnetic field gradient of each sampling point is calculated according to the electromagnetic field spatial distribution data, and the time-varying characteristic data of the interference source is obtained by combining the independent signal source data and extracting the time-domain mutation characteristics and periodic characteristics. The characteristic space probability distribution of the interference source time-varying characteristic data is established by the kernel density estimation method, and is fused with the optimized interference source probability distribution model to realize the characteristic representation and spatial positioning of various interference sources. The electromagnetic sensor array adopts a 4×4 matrix layout with a spacing of 30 cm between adjacent sensors, covering an area of 100 square meters in the laboratory. The sampling frequency of each sensor is set to 1000 Hz, the measurement frequency range is 10 Hz to 100 kHz, and the sensitivity reaches 0.1 mV / m. The original electromagnetic field data includes amplitude and phase dimensions, and the frequency spectrum characteristics are obtained by 1024-point fast Fourier transform with a frequency resolution of 0.98 Hz. In the frequency spectrum characteristic data, the peak detection method is used to extract the characteristic frequency components, and the dynamic threshold is set to 3 times the signal mean value to identify the significant frequency peaks. In a certain laboratory, the main interference frequencies detected include the power frequency 50 Hz and its harmonics, the switching power supply 20 kHz, and the digital device clock 100 MHz. Independent component analysis is performed on these characteristic frequency data, and the FastICA algorithm is used to separate 5 independent signal sources. The number of Gaussian components is set to 8 during the initialization of the mixed Gaussian model, and each component contains two parameters: mean vector and covariance matrix. The parameters are optimized by the expectation maximization algorithm, and the convergence threshold is set to 0.001 with a maximum of 100 iterations. During the optimization process, a Gaussian component is automatically removed when its weight is less than 0.05, and finally 6 effective Gaussian components are obtained. In the triangular positioning, the positioning equation is constructed by selecting the three sensor nodes with the highest signal intensity, and the distance of the interference source is calculated based on the signal intensity inverse attenuation model. In the laboratory environment, the positioning accuracy reaches 0.5 meters, and the electromagnetic field distribution data within a range of 10×10×3 meters is obtained by spatial interpolation with a spatial resolution of 0.1 meters. The electromagnetic field gradient is calculated using the central difference method to calculate the field intensity change rate of adjacent sampling points.The time-domain feature extraction focuses on the signal amplitude variation. When the signal mutation amplitude exceeds 2 times the average value, it is marked as a mutation point. The time interval that repeatedly appears in the continuous sampling is taken as the period characteristic. The start-up process of a desktop computer is observed to have obvious mutation characteristics, with a peak value reaching 5 times the background field strength, accompanied by a 50 ms periodic pulse signal. The kernel density estimation adopts a Gaussian kernel function, and the kernel width parameter is determined to be 0.15 through cross-validation. The feature space probability distribution fusion adopts a Bayesian framework, and the same weight is given to the time-varying feature and the spatial distribution feature. In the laboratory scene, different types of interference sources such as printers, air conditioners, and computers are successfully distinguished, and the interference source type recognition accuracy reaches 90%, and the spatial positioning error is less than 0.8 meters. In a certain research and development laboratory, the electromagnetic interference generated by 15 different types of electronic equipment is monitored for a long time, a complete interference source feature library is established, and the rapid identification and positioning of new interference sources are realized. During the operation of the system, the amount of data collected is more than 100 GB, and the feature library contains more than 1000 typical interference characteristics.

[0021] In S103, the standardized electromagnetic characteristic data of the electronic equipment, the real-time environmental data of the sensor network, and the running state data of the electronic equipment are fused and input into the trained and optimized interference source probability distribution model to predict and output the electromagnetic environment perception result, and the electromagnetic environment perception result is visualized and analyzed.

[0022] According to the preset normalization parameter, the electromagnetic characteristic data, the environmental data and the state data are processed by maximum and minimum value normalization to obtain an electromagnetic data set, an environmental data set and a state data set; waveform amplitude and phase characteristics are extracted from the electromagnetic data set, temperature and humidity and illumination parameters are extracted from the environmental data set, and voltage and current working condition parameters are extracted from the state data set; a feature vector is established through a corresponding time stamp; a weighted summation method is used for data fusion of the feature vector, and a fusion feature vector is obtained according to electromagnetic feature weights, environmental parameter weights and state parameter weights; a mixed Gaussian probability density function is calculated through the fusion feature vector, Gaussian component parameters are updated online, and if the difference between the new data and the historical distribution exceeds a preset threshold, the Gaussian component parameter update is triggered; a heat map is constructed according to the electromagnetic environment distribution data, the abnormal area of the field strength is marked, and an isopleth visualization chart is generated.

[0023] Specifically, the device electromagnetic characteristic data is subjected to maximum-minimum value standardization processing according to preset normalization parameters to obtain an electromagnetic data set, real-time environmental data of the sensor network is subjected to maximum-minimum value standardization processing to obtain an environmental data set, and the electronic device operating state data is subjected to maximum-minimum value standardization processing to obtain a state data set. Waveform amplitude and phase features are extracted from the electromagnetic data set, temperature and humidity and illumination parameters are extracted from the environmental data set, and voltage and current working condition parameters are extracted from the state data set, and a feature vector is established according to the corresponding time stamp. A weighted summation method is used for data fusion of the feature vector, the electromagnetic feature weight is set to 0.5, the environmental parameter weight is set to 0.3, and the state parameter weight is set to 0.2, and a fused feature vector is generated. The fused feature vector is input into a mixed Gaussian probability density function, the probability distribution of the electromagnetic field intensity of each sampling point is calculated, and the Gaussian component parameters are optimized by an expectation maximization method. The Gaussian component parameters are updated online based on a stochastic gradient descent method, and the parameter update is triggered when the difference between the new data and the historical distribution exceeds a preset threshold. A space grid is established in a three-dimensional rectangular coordinate system, the expected value and variance of the electromagnetic field intensity are calculated for each grid node, and electromagnetic environment distribution data is generated. A heat map is constructed according to the electromagnetic environment distribution data, the field intensity abnormal area is marked, the field intensity gradient vector is calculated, and an isopleth visualization chart is generated. The data is normalized by a maximum-minimum value standardization method, the original field intensity range of the electromagnetic characteristic data is 0-120 mV / m, which is mapped to the 0-1 interval, the temperature in the environmental data is mapped to the 0-1 interval from -10 to 40 degrees Celsius, the humidity is mapped to the 0-1 interval from 30% to 90%, the voltage in the device operating state is mapped to the 0-1 interval from 180V to 250V, and the current is mapped to the 0-1 interval from 0 to 50A. Features are extracted from the normalized data, the electromagnetic waveform features include peak-to-peak value, root mean square value, and peak factor, and the phase features include phase angle and phase stability. The temperature change rate, humidity change rate, and illumination intensity change rate are extracted as environmental parameters. The voltage fluctuation rate, current fluctuation rate, and power factor are extracted as device operating state parameters. All features are aligned according to millisecond-level time stamps to form a 32-dimensional feature vector. Data fusion adopts a weighted summation method, and the weight distribution is set based on domain rules. The electromagnetic feature weight is higher, reflecting the importance of electromagnetic environment monitoring, the environmental parameter is second, reflecting external influences, and the device state parameter weight is the lowest, serving as an auxiliary judgment basis. In a certain laboratory scene, the fused feature vector shows that the electromagnetic field intensity and temperature are positively correlated, and the electromagnetic field intensity and humidity are negatively correlated. When the mixed Gaussian model is initialized, six Gaussian components are set, each component includes a 32-dimensional mean vector and a 32x32 covariance matrix. The parameters are iteratively optimized by an expectation maximization algorithm, the convergence threshold is set to 0.001, and the maximum iteration number is set to 200. In a certain experimental scene, four significant Gaussian components are retained after the model converges, corresponding to different working states.Mini-batch is used in online learning, and parameter update is triggered after collecting 100 new data sets each time. The initial learning rate is set to 0.01, which gradually decreases as the data volume increases. When the KL divergence between new data and historical distribution exceeds 0.5, the learning rate is increased to speed up model adaptation. In the experiment, it is observed that the model parameters are updated rapidly in scenarios such as device startup and load change. The spatial grid is divided into 0.5-meter resolution, generating 960 grid nodes within a 10x8x3-meter space. Each node calculates the expected value and 95% confidence interval of the electromagnetic field strength, generating spatial distribution data. In a certain area of the laboratory, the field strength anomaly occurs, with an expected value of 5 times the background value and a significant increase in variance. Visualization uses a red-yellow-blue three-color heat map, with blue representing field strength less than 1 mV / m, yellow representing 1-5 mV / m, and red representing greater than 5 mV / m. The field strength gradient is marked with arrows, with the arrow length proportional to the gradient size. The contour interval is set to 1 mV / m, clearly showing the field strength distribution profile. In an industrial site application, the heat map visually displays the electromagnetic field distribution within 3 meters around the device, identifying 2 field strength anomaly points.

[0024] In S104, based on the predicted electromagnetic environment perception result, electromagnetic environment feature data is extracted, different dimensions of electromagnetic environment feature data are denoised and preprocessed, weight allocation and optimization combination are performed, and the position coordinates of the interference source are obtained through iterative calculation.

[0025] The electromagnetic field strength feature data, phase difference feature data, frequency distribution feature data, and signal attenuation feature data are obtained from the electromagnetic environment perception result, and the filtered feature data is obtained through wavelet denoising and median filtering processing. The feature covariance matrix is calculated according to the filtered feature data, and the dimension-reduced feature data is obtained by selecting the feature vector corresponding to the cumulative contribution rate exceeding the contribution rate threshold for the feature covariance matrix. The initial weight is allocated to the dimension-reduced feature data according to the spatial electromagnetic propagation attenuation law, and the optimal feature weight coefficient is obtained by iterative optimization of the feature weight through the gradient descent method. The coarse coordinates of the interference source are calculated by weighting the optimal feature weight coefficient, the search space is constructed according to the coarse coordinates, and the precise coordinates of the interference source are obtained by solving the position optimization equation using the Newton iteration method.

[0026] Specifically, the electromagnetic field intensity, phase difference, frequency distribution, and signal attenuation four-dimensional feature data are extracted from the electromagnetic environment perception results. The first type of feature data is obtained by wavelet denoising processing of the extracted data, and the second type of feature data is obtained by eliminating outliers through median filtering. The feature covariance matrix is calculated based on the second type of feature data, and the feature vectors corresponding to the cumulative contribution rate exceeding the preset threshold are selected to perform dimension reduction conversion on the second type of feature data to obtain the third type of feature data. Based on the spatial electromagnetic propagation attenuation law, an initial feature weight allocation scheme is constructed, and the initial weights are assigned to the field intensity, phase, frequency, and attenuation dimensions of the third type of feature data. The least mean square criterion is used to establish a weight optimization objective function, and the gradient descent method is used to iteratively optimize the feature weights to obtain the optimal feature weight coefficients. The third type of feature data is weighted and combined based on the optimal feature weight coefficients to construct a target function based on the electromagnetic field intensity distribution, and a particle swarm optimization algorithm is used to calculate the rough coordinates of the interference source position. A search space is constructed around the rough coordinates of the interference source position, and a least squares position optimization equation is established based on the signal intensity received by multiple sensor nodes in a three-dimensional space. The Newton iteration method is used to solve the position optimization equation, and the iteration is stopped when the position coordinate update is less than the preset threshold, and the accurate position coordinates of the interference source are output. The electromagnetic environment perception results contain multi-dimensional feature data, including electromagnetic field intensity ranging from 0 to 100 mV / m, phase difference ranging from 0 to 360 degrees, frequency distribution containing 50 Hz power frequency and its harmonics, and signal attenuation following the inverse square law. For these raw data, db4 wavelet basis function is used for 4-layer decomposition and denoising to effectively suppress high-frequency random noise. The 5-point sliding window median filter successfully eliminates 95% of outliers, and the signal-to-noise ratio after processing is improved by 8 dB. The feature covariance matrix calculation reflects the correlation between the dimensions of the features, with a correlation coefficient of 0.72 between field intensity and phase, and a correlation coefficient of 0.45 between field intensity and frequency. The cumulative contribution rate threshold is set to 0.85, and finally 3 principal component feature vectors are selected, with the original information retention rate reaching 87% after dimension reduction. The spatial electromagnetic propagation attenuation law shows that the field intensity attenuation rate is inversely proportional to the square of the distance, and the phase difference increases linearly with the propagation distance. Accordingly, the initial weights of the field intensity feature, phase feature, frequency feature, and attenuation feature are set to 0.4, 0.3, 0.2, and 0.1, respectively. The least mean square criterion is used to optimize the weights, and the learning rate is set to 0.01. After 200 iterations, the weight coefficients converge. In the particle swarm optimization algorithm, the number of particles is set to 50, the search space is set to 10x10x3 meters, and the particle velocity is limited to 0.1-1 meter / second. Taking a laboratory scene as an example, under the condition of 5 sensor node arrangements, the particle swarm optimization algorithm obtains the rough coordinates of the interference source position, with a positioning error of less than 1 meter. The least squares position optimization uses the Gauss-Newton iteration method, and the particle swarm optimization result is used as the initial value to construct a local search space of 3x3x1 meters.The over-determined equation set is established according to the signal strength measured by the five sensor nodes, and the convergence is determined when the coordinate update is less than 1 cm in the iterative optimization process. In the laboratory verification, 100 repeated positioning tests are carried out for the fixed position interference source, and the average positioning error is 0.15 meters, and the maximum error is not more than 0.3 meters. Taking an industrial site application as an example, 8 sensor nodes are arranged in a 100 square meter workshop, and 3 moving interference sources are positioned in real time. The original electromagnetic field data sampling rate is 1000 Hz, and through the data processing chain of noise reduction, dimensionality reduction and optimization, the position of the interference source is realized in millisecond level. The positioning result shows that the positioning accuracy of the static interference source is better than 0.2 meters, and the positioning accuracy of the moving interference source (speed less than 1 meter / second) is better than 0.5 meters. The method has good resolution ability to multi-source interference in complex electromagnetic environment, and the accuracy reaches 92% in the case of coexistence of different types of interference sources such as power frequency equipment, switching power supply and frequency converter. During the system operation, the average delay of data processing is less than 50 milliseconds, which meets the real-time monitoring and positioning requirements.

[0027] S105, according to the coordinates of the interference source position, the array of the sensor network is evaluated in different regions, the regions with low detection coverage or signal quality are identified, the sensor position and orientation in the preset range of the identified region are adjusted, and the sensitivity parameters of the sensor are adjusted according to the electromagnetic environment perception results, and in the adjustment process, the detection effect of each region is evaluated and analyzed, and the sensor array layout and parameters are optimized.

[0028] According to the coordinates of the interference source position, a grid evaluation region is constructed, the signal coverage of the region is calculated by using the detection radius of the sensor node, and the region with a signal coverage lower than a coverage threshold is marked as an optimization region; the signal reliability value is calculated according to the signal-to-background noise ratio of the sensor node in the optimization region, and the sensor sensitivity adjustment parameter is generated according to the signal reliability value; the coverage optimization function and the signal strength optimization function are constructed by using the sensitivity adjustment parameter, and the sensor node position coordinates and direction angle in the optimization region are optimized to obtain the sensor layout optimization parameter; the sensor node configuration database is established according to the sensor layout optimization parameter, and the update instruction is issued to the sensor node through the database content to obtain the optimized sensor array configuration.

[0029] Specifically, a grid evaluation area is constructed based on the coordinates of the interference source location, the signal coverage of the area is calculated according to the detection radius of each sensor node, and the area with a signal coverage lower than a preset threshold is marked as an optimization area. An electromagnetic field intensity distribution data of the optimization area is measured by a signal strength calculator. The performance of the sensor nodes in the optimization area is measured, the signal reliability value is obtained by calculating the ratio of the received signal to the background noise, and the sensor sensitivity adjustment parameter is generated according to the signal reliability value. An optimization algorithm is used to construct a coverage optimization function and a signal strength optimization function, and the position coordinates and direction angles of the sensor nodes in the optimization area are jointly optimized to generate sensor layout optimization parameters. A sensor node configuration database is established based on the layout optimization parameters, and the sensor position coordinates, direction angles, and sensitivity parameters are recorded to construct a sensor parameter optimization record table. A data fusion method is used to evaluate the online quality of the sensor data, calculate the signal coverage and field strength detection accuracy of the area, and generate sensor performance evaluation data. According to the sensor performance evaluation data, the optimization result is determined, and when the signal coverage and field strength detection accuracy of the area meet the preset threshold, the sensor configuration data is written into the parameter configuration database. The feedback controller reads the content of the parameter configuration database and sends an update instruction to each sensor node to complete the sensor array parameter configuration. The grid evaluation area is divided by 0.5-meter intervals, forming 400 evaluation grid points in a 100-square-meter laboratory space. The sensor node detection radius is set to 3 meters, and the signal coverage distribution map is calculated by spatial superposition. When the coverage is less than 70%, it is marked as an optimization area. In a certain laboratory scene, three optimization areas with a total area of about 15 square meters are identified, mainly distributed in the corners of the wall and equipment-intensive areas. The sensor node performance measurement uses a signal-to-noise ratio evaluation method, and the effective signal strength range is 0.5-10 mV / m under the condition of background noise of 0.1 mV / m. The signal reliability value is calculated in sections, with a signal-to-noise ratio greater than 20 dB recorded as 1.0, 10-20 dB recorded as 0.8, and 5-10 dB recorded as 0.5. For low-reliability areas, the sensor sensitivity parameter is increased by 20% based on the original value, and the signal sampling time is also increased. In the joint optimization calculation, the weight of the coverage optimization function is set to 0.6, and the weight of the signal strength optimization function is set to 0.4. The position optimization range is limited to within 1 meter of the original position, and the direction angle adjustment range is plus or minus 30 degrees. The optimization results show that by adjusting the positions of two sensors and the direction angles of three sensors, the average coverage of the optimization area is improved to 85%. The parameter configuration database uses a key-value storage structure to record sensor identification numbers, spatial coordinates, direction angles, gain coefficients, and other parameters. The database supports incremental updates, and only the changed parameter items are updated after each optimization. In a certain industrial site application, the database capacity reaches 100 MB, containing parameter optimization records for one year.The online quality evaluation adopts a sliding window method, and the window length is set to 1 hour. The average coverage and detection accuracy are calculated. The coverage threshold is set to 80%, and the accuracy threshold is set to 90%. When the evaluation index continuously exceeds the threshold for 4 hours, the parameter configuration update process is triggered. Experimental data shows that the optimized sensor array maintains a stable working state for 95% of the time. The parameter configuration update adopts a distributed collaborative mechanism, first sends a pre-configuration instruction to the target sensor, and executes the formal update after receiving the confirmation. A double-buffer mechanism is used in the update process to ensure data continuity. In a certain field application, the total time for parameter update of 12 sensor nodes does not exceed 10 seconds, and there is no interruption during data acquisition. The feedback control adopts a proportional-integral algorithm, which adjusts the sensor parameters according to the coverage deviation and cumulative error. The control period is set to 1 minute, the proportional coefficient is 0.8, and the integral time is 10 minutes. In actual operation, the response time of the system to environmental changes is less than 5 minutes, and the parameter adjustment process is smooth without oscillation. Statistical data shows that the optimized sensor network reduces the annual average number of faults by 60% in industrial field applications, reduces the maintenance cost by 50%, and maintains the detection accuracy above 95%.

[0030] The position and orientation information of the sensors within the preset range in the identification area are obtained, and a plurality of groups of candidate sensor adjustment schemes are generated. The detection coverage and signal quality of the adjusted sensors are simulated and calculated for the candidate adjustment schemes, and compared with the preset optimization target to select a target adjustment scheme. If the target adjustment scheme meets the preset optimization target, the sensor position and orientation adjustment parameters in the scheme are issued to the corresponding sensors.

[0031] The spatial positioner reads the three-dimensional coordinate and direction angle data of the sensor nodes, and obtains the detection coverage distribution data according to the sensor detection radius. The sensor position adjustment range and direction adjustment angle are set according to the detection coverage distribution data, and the sensor adjustment candidate scheme is obtained by the Monte Carlo sampling method. The signal coverage rate is calculated for the sensor adjustment candidate scheme, the numerical integration method is used to calculate the detection area signal coverage rate score and the area signal quality score, and the optimization target function is constructed according to the preset weight coefficient. The sensor adjustment candidate scheme is iteratively optimized, and if the optimization target function value exceeds the function value threshold, the adjustment instruction of the sensor position coordinate and direction angle is generated.

[0032] Specifically, the spatial locator reads the three-dimensional coordinates and direction angle data of the sensor nodes within a preset range, generates detection coverage distribution data according to the detection radius of the sensor, and measures and records the signal quality distribution data of the detection area through the signal strength calculator. Based on the detection coverage distribution data, the sensor position adjustment range and direction adjustment angle are set, and a plurality of groups of sensor adjustment candidate schemes are generated through the Monte Carlo sampling method. A signal coverage rate calculation model is established for each group of candidate schemes, the signal coverage rate score of the detection area is calculated by using the numerical integration method, and the regional signal quality score is calculated according to the signal strength threshold. The signal coverage rate score of the detection area is assigned a weight coefficient of 0.6, and the regional signal quality score is assigned a weight coefficient of 0.4, and an optimization objective function of the candidate scheme is constructed. The particle swarm optimization algorithm is used to iteratively optimize the candidate scheme, and the position constraint condition and the direction constraint condition are set, and the iteration is stopped when the optimization objective function value exceeds the preset threshold. The optimized sensor position coordinates and direction angle data are written into the adjustment instruction, and the parameter adjustment instruction is issued to the corresponding sensor through the communication network. The online monitoring method is used to track the sensor adjustment execution process, record the sensor position and direction adjustment data, and generate an execution process record. Based on the execution process record, the actual adjusted detection coverage rate and signal quality indicators are calculated, and the sensor adjustment scheme execution is completed when the calculation result meets the preset optimization target. The spatial locator uses the ultrasonic ranging principle, arranges 4 positioning base stations in the laboratory, and the measurement accuracy is better than 5mm. The initial arrangement of the sensor nodes uses a 4×4 matrix layout, the distance between adjacent sensors is 2m, and the detection radius is set to 3m, forming a regional coverage grid. Through signal strength calculation, the signal strength of 78% of the positions in the coverage area is greater than 1mV / m, and 22% of the positions are below the threshold. The sensor position adjustment range is limited within a 1.5m radius sphere centered at the origin, and the direction angle adjustment range is 45 degrees in the horizontal direction and 30 degrees in the vertical direction. 1000 Monte Carlo samples are used to generate adjustment candidate schemes within the constraint range. The position coordinates of each sampling point are generated by a three-dimensional Gaussian distribution, and the direction angle is generated by a uniform distribution. After eliminating the schemes that do not meet the constraint conditions, 856 valid candidate schemes are retained. The signal coverage rate calculation uses the grid division method to divide the detection area into 0.5m×0.5m grids, and calculates whether the received signal of each grid point exceeds the detection threshold. The signal quality score is calculated based on the signal-to-noise ratio, and the ratio of the measured signal strength to the background noise is mapped to the interval [0, 1]. In a certain industrial environment, the average background noise is 0.2mV / m, the effective signal threshold is set to 1mV / m, and the signal-to-noise ratio greater than 14dB is recorded as full score. The optimization objective function considers both coverage rate and signal quality, with higher weight on coverage rate to reflect the importance of spatial coverage, and lower weight on signal quality as an auxiliary judgment basis. The particle swarm optimization is set to 50 particles, 200 maximum iterations, and the calculation is stopped when the objective function value exceeds 0.85 or the improvement is less than 0.001 for 20 consecutive iterations.The parameter adjustment instruction adopts a standardized data format and contains a sensor identification number, a target position coordinate, a target direction angle, and an adjustment time window. The communication network adopts a star topology and realizes reliable transmission of instructions through an industrial Ethernet, with an average transmission delay of less than 10 milliseconds. Online monitoring adopts real-time data stream processing, and position and direction data of the sensor are collected every 50 milliseconds to record the adjustment trajectory. The execution process record contains the start and end time of adjustment, the position change curve, the direction change curve, and the signal quality change curve. In a certain laboratory scene, the average time consumption of sensor adjustment is 45 seconds, and the adjustment accuracy is better than 1 centimeter. Based on the execution effect evaluation data, the signal strength of 92% of the detection area after adjustment exceeds the threshold value, and the average signal-to-noise ratio is improved by 6 dB. Through 48 hours of continuous monitoring of the sensor network, the stability of the adjustment scheme is verified, and the fluctuation range of the signal coverage rate and quality index is controlled within 5%. In practical applications, this method has good adaptability to environmental changes and can quickly complete sensor optimization adjustment in scenarios such as changes in device layout and the appearance of electromagnetic interference sources.

[0033] S106, apply the optimized sensor array layout and parameter settings to the test process of the electronic device, obtain the performance parameters of the electronic device in different electromagnetic environments, including electromagnetic compatibility and anti-interference ability, and compare and calibrate the test results with the preset standard value.

[0034] The electromagnetic field intensity data is collected by the sensor array, and the performance parameters of the electronic device are recorded according to the electromagnetic field intensity data, including output voltage, output current and output power. The electromagnetic field intensity data is subjected to Fourier transform to obtain frequency domain characteristic data representing electromagnetic field distribution characteristics. Electromagnetic compatibility indicators are extracted according to the device performance parameters to obtain an electromagnetic compatibility feature vector, including stray emission level, conducted interference level and radiation sensitivity. A mapping relationship between the electromagnetic compatibility feature vector and the anti-interference feature vector is established to obtain a device performance change trend prediction result. The performance deviation is calculated according to the prediction result and the preset standard value, and the sensitivity parameters of the sensor array are compensated and adjusted.

[0035] Specifically, the electromagnetic field test space is constructed according to the optimized sensor array layout, the environmental electromagnetic field data is collected from the sensor nodes, the output voltage, current, power and other performance parameters of the electronic device under different electromagnetic intensities are recorded by the data collector, and the collected data is time-sequentially recorded and labeled. The performance parameters are collected by using the variable sampling rate method, the sampling frequency is increased in the signal change interval, the collected data is wavelet denoising processed to obtain the device operation data, and the frequency domain characteristic data is obtained by Fourier transform of the electromagnetic field intensity data. The electromagnetic compatibility indicators are extracted from the device operation data, including the stray emission level, the conducted interference level, and the radiation sensitivity, and the electromagnetic compatibility feature vector is generated. The electromagnetic interference intensity distribution is calculated based on the frequency domain characteristic data, the signal detector is used to measure the influence degree of external electromagnetic interference on the device performance, and the anti-interference feature vector is generated. The mapping relationship between the electromagnetic compatibility feature vector and the anti-interference feature vector is established by using the support vector regression method, and the performance change trend of the device under different electromagnetic environments is predicted. According to the prediction result and the preset standard value, the performance deviation is calculated, the calibration compensation function is constructed, and the sensor array sensitivity parameters are compensated and adjusted. The test results are corrected by the calibration compensation function, and when the error between the corrected performance parameters and the standard value is less than the preset threshold, the calibration is completed. The electromagnetic field test space adopts an octahedral structure with a side length of 6 meters, and the sensor nodes are uniformly distributed at the eight vertices according to the space uniform distribution principle, realizing omnidirectional electromagnetic field monitoring. The performance parameter collection includes output voltage range 0-380V, current range 0-100A, power factor range 0-1, and sampling resolution reaches 16 bits. In a certain industrial equipment test, the voltage fluctuation amplitude is 0.5V, and the current fluctuation amplitude is 0.2A. In the variable sampling rate method, the basic sampling frequency is set to 1kHz, and when the signal change rate exceeds 10%, it is automatically increased to 10kHz, realizing accurate capture of transient process. The wavelet denoising uses db4 wavelet basis function and 4-layer decomposition, successfully suppressing 95% random noise. Fourier transform uses 1024-point FFT, frequency resolution is better than 1Hz, and the device working characteristic frequency is identified in the range of 0-100kHz. In the extraction of electromagnetic compatibility indicators, the stray emission level measurement range is 30MHz-1GHz, and the typical value is lower than 30dBμV / m. The conducted interference level is measured in the frequency band of 150kHz-30MHz, and the typical value is lower than 60dBμV. The radiation sensitivity test field strength is set to 3V / m, the frequency steps 20MHz, and the device performance change is recorded. The feature vector contains 50 dimensions, covering the key frequency band interference characteristics. The electromagnetic interference intensity distribution calculation adopts the space interpolation method, and the grid resolution is 0.2 meters, generating a three-dimensional field strength distribution map. The signal detector sensitivity is 0.1mV / m, the background interference is 3mV / m under power frequency working condition, the switching power supply interference is 8mV / m, and the frequency converter interference is 15mV / m. The anti-interference feature vector records the response characteristics of the device under the action of different interference sources. The support vector regression adopts radial basis kernel function, and the kernel parameter is optimized by cross-validation.The training data contains 1000 sets of performance test records, covering typical and extreme working conditions. The prediction results show that the device performance index and the electromagnetic environment present a nonlinear relationship, and the performance degradation rate accelerates in a strong interference environment. The calibration compensation function uses piecewise linear interpolation, and sets compensation coefficients at key points. The sensor sensitivity adjustment range is 80%-120% of the nominal value, with a step accuracy of 1%. In a certain test scenario, the original measurement error reaches 15%, and after compensation, it is reduced to within 3%. The calibration process uses an iterative method, and when the measurement error is less than 5% for three consecutive times, the calibration is completed. Practical application verification shows that the test method has achieved good results on different types of equipment. For industrial frequency converters, 12 times of electromagnetic interference anomalies are successfully identified in 72 hours of continuous testing, and the trend of device performance change is accurately predicted. The repeatability error of the test results is controlled within 2%, meeting the requirements of metrological certification.

[0036] In addition, it should be noted that various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction, and in order to avoid unnecessary repetition, various possible combinations are not described again by the present application. Furthermore, various different embodiments of the present application can also be combined in any manner, as long as they do not violate the spirit of the present application, and they should also be considered as disclosed by the present application.

Claims

1. A multi-band EMI electromagnetic interference source identification and positioning method based on, characterized in that, The method comprises: The acquired electromagnetic characteristic data, real-time environmental data of the sensor network and operating state data of the electronic device are preprocessed and standardized; By collecting electromagnetic field intensity data generated by various operating equipment in the laboratory, frequency characteristics of different interference sources, time-varying characteristics of electromagnetic signals and spatial distribution relationship information of electronic devices, an interference source probability distribution model for distinguishing and identifying multi-source interference is constructed, and each interference source in the laboratory electromagnetic environment is characterized and spatially positioned; The standardized processed electromagnetic characteristic data of the electronic device, real-time environmental data of the sensor network and operating state data of the electronic device are fused, input into the trained and optimized interference source probability distribution model, and electromagnetic environment perception results are predicted and output, and the electromagnetic environment perception results are visualized and analyzed; Based on the predicted electromagnetic environment perception results, electromagnetic environment feature data is extracted, different dimensions of electromagnetic environment feature data are denoised and preprocessed, and weight allocation and optimized combination are performed, and the position coordinates of the interference sources are obtained through iterative calculation; According to the position coordinates of the interference sources, the array of the sensor network is evaluated in different regions, the regions with detection coverage or signal quality lower than the preset requirements are identified, the sensor positions and orientations in the preset range of the identified regions are adjusted, the sensitivity parameters of the sensors are adjusted according to the electromagnetic environment perception results, and in the adjustment process, the detection effect of each region is evaluated and analyzed, and the sensor array layout and parameters are optimized; The optimized sensor array layout and parameter settings are applied to the test process of the electronic device, the performance parameters of the electronic device in different electromagnetic environments are obtained, the performance parameters include electromagnetic compatibility and anti-interference ability, and the test results are compared and calibrated with the preset standard values.

2. The method of claim 1, wherein, The method comprises: The electromagnetic characteristic data of the electronic device is acquired from a plurality of electromagnetic sensing nodes, and the electromagnetic characteristic data records time sequence information at a preset sampling frequency; The first type of processed data is obtained by wavelet denoising processing according to the electromagnetic characteristic data, and the second type of processed data is obtained by mean filtering the environmental temperature and humidity data and the illumination intensity data collected by the distributed sensor network; The first type of processed data and the second type of processed data are time-aligned by time stamp, and the correlation degree between the first type of processed data and the second type of processed data is calculated to obtain an abnormal data set; The abnormal data set is segmented by using a fixed-length sliding time window, the segmented data is clustered to obtain a device state feature vector, and a running state corresponding relationship database is established according to the device state feature vector.

3. The method of claim 1, wherein, The method comprises: The method comprises: The original electromagnetic field data collected by the distributed electromagnetic sensor array is acquired, and Fourier transform is performed on the original electromagnetic field data to obtain frequency spectrum feature data; According to the frequency spectrum feature data, a first type of feature data is extracted from the frequency components of the interference source features, and independent component analysis is performed on the first type of feature data to obtain independent signal source data; According to the independent signal source data, a mixed probability density function with multiple Gaussian components is constructed, and the Gaussian component parameters are updated by expectation maximization iteration to obtain an interference source probability distribution model; A triangular positioning method is used to spatially locate each Gaussian component in the interference source probability distribution model, and signal strength data is obtained from the electromagnetic sensor array for spatial interpolation processing to obtain electromagnetic field spatial distribution data; According to the electromagnetic field spatial distribution data, the electromagnetic field gradient of each sampling point is calculated, and the time-varying feature data of the interference source is extracted by combining the independent signal source data and the time-domain mutation feature and the periodic feature.

4. The method of claim 1, wherein, The normalized electromagnetic characteristic data of the electronic device, the real-time environmental data of the sensor network, and the running state data of the electronic device are fused, input into the trained and optimized interference source probability distribution model, and the electromagnetic environment perception result is predicted and output, and the electromagnetic environment perception result is visualized and analyzed, including: According to the preset normalization parameters, the maximum and minimum value normalization processing is performed on the electromagnetic characteristic data, the environmental data and the state data to obtain the electromagnetic data set, the environmental data set and the state data set; Waveform amplitude and phase features are extracted from the electromagnetic data set, temperature and humidity and illumination parameters are extracted from the environmental data set, and voltage and current working condition parameters are extracted from the state data set, and a feature vector is established through the corresponding time stamp; The feature vectors are fused by using a weighted summation method, and a fused feature vector is obtained according to the electromagnetic feature weight, the environmental parameter weight and the state parameter weight; The mixed Gaussian probability density function is calculated through the fused feature vector, the Gaussian component parameters are updated online, and if the difference between the new data and the historical distribution exceeds the preset threshold, the Gaussian component parameter update is triggered; According to the electromagnetic environment distribution data, a heat map is constructed, the abnormal area of the field strength is marked, and an isopleth visualization chart is generated.

5. The method of claim 1, wherein, Based on the predicted electromagnetic environment perception result, electromagnetic environment feature data is extracted, different dimensions of electromagnetic environment feature data are denoised and preprocessed, and weight allocation and optimization combination are performed, and the position coordinates of the interference source are obtained through iterative calculation, including: Electromagnetic field strength feature data, phase difference feature data, frequency distribution feature data and signal attenuation feature data are obtained from the electromagnetic environment perception result, and filtered feature data is obtained through wavelet denoising and median filtering processing; According to the filtered feature data, a feature covariance matrix is calculated, and a feature vector corresponding to a cumulative contribution rate exceeding a contribution rate threshold is obtained from the feature covariance matrix to obtain reduced dimension feature data; According to the reduced dimension feature data, an initial weight is allocated according to the spatial electromagnetic propagation attenuation law, and the feature weight is iteratively optimized by the gradient descent method to obtain the optimal feature weight coefficient. The interference source rough coordinate is obtained by calculating the feature data weighted by the optimal feature weight coefficient, and the search space is constructed according to the rough coordinate and the position optimization equation is solved by using Newton iteration method to obtain the accurate coordinate of the interference source.

6. The method of claim 1, wherein, According to the position coordinates of the interference source, the array of the sensor network is evaluated in a region-by-region manner, and the area where the detection coverage or signal quality is lower than the preset requirement is identified. The position and orientation of the sensor within the preset range of the identified area are adjusted, and the sensitivity parameter of the sensor is adjusted according to the electromagnetic environment perception result. During the adjustment process, the detection effect of each area is evaluated and analyzed, and the sensor array layout and parameters are optimized, including: According to the position coordinates of the interference source, a grid evaluation area is constructed, the signal coverage of the area is calculated using the detection radius of the sensor node, and the area with a signal coverage lower than a coverage threshold is marked as an area to be optimized. The signal reliability value is calculated by collecting the signal-to-background noise ratio of the sensor node in the area to be optimized, and the sensor sensitivity adjustment parameter is generated according to the signal reliability value. The coverage optimization function and the signal strength optimization function are constructed using the sensitivity adjustment parameter, and the position coordinates and direction angle of the sensor node in the area to be optimized are optimized to obtain the sensor layout optimization parameter. According to the sensor layout optimization parameter, a sensor node configuration database is established, and an update instruction is issued to the sensor node through the database content to obtain the optimized sensor array configuration. Further comprising: obtaining the position and orientation information of the sensor within the preset range in the identified area, generating a plurality of candidate sensor adjustment schemes, simulating the detection coverage and signal quality of the adjusted sensor for the candidate adjustment schemes, and comparing them with the preset optimization target to select the target adjustment scheme. If the target adjustment scheme meets the preset optimization target, the sensor position and orientation adjustment parameters in the scheme are issued to the corresponding sensor.

7. The method of claim 6, wherein, The position and orientation information of the sensor within the preset range in the identified area is obtained, a plurality of candidate sensor adjustment schemes are generated, the detection coverage and signal quality of the adjusted sensor are simulated for the candidate adjustment schemes, and the target adjustment scheme is selected by comparing them with the preset optimization target. If the target adjustment scheme meets the preset optimization target, the sensor position and orientation adjustment parameters in the scheme are issued to the corresponding sensor, including: The three-dimensional coordinates and direction angle data of the sensor node are read by a spatial positioner, and the detection coverage distribution data is obtained according to the sensor detection radius. The sensor position adjustment range and direction adjustment angle are set according to the detection coverage distribution data, and the sensor adjustment candidate scheme is obtained by the Monte Carlo sampling method. The signal coverage is calculated for the sensor adjustment candidate scheme, the signal coverage score and the area signal quality score are calculated by numerical integration method, and the optimization target function is constructed according to the preset weight coefficient. The sensor adjustment candidate scheme is iteratively optimized, and if the optimization target function value exceeds the function value threshold, an adjustment instruction for the sensor position coordinates and direction angle is generated.

8. The method of claim 1, wherein, The optimized sensor array layout and parameter setting are applied to the test process of the electronic device, the performance parameters of the electronic device in different electromagnetic environments are obtained, the performance parameters include electromagnetic compatibility and anti-interference ability, and the test results are compared and calibrated with the preset standard value, including: The electromagnetic field intensity data is collected by the sensor array, the performance parameters of the electronic device are recorded according to the electromagnetic field intensity data, and the performance parameters include output voltage, output current and output power; The Fourier transform is performed on the electromagnetic field intensity data to obtain frequency domain characteristic data, and the frequency domain characteristic data represents the electromagnetic field distribution characteristics; According to the device performance parameters, the electromagnetic compatibility index is extracted to obtain the electromagnetic compatibility feature vector, and the electromagnetic compatibility index includes the level of stray emission, the level of conducted interference and the radiation sensitivity; The mapping relationship between the electromagnetic compatibility feature vector and the anti-interference feature vector is established to obtain the device performance change trend prediction result; According to the prediction result and the preset standard value, the performance deviation is calculated, and the sensor array sensitivity parameter is compensated and adjusted.

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